Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features.

Rongna Lian, Huiyu Tang, Zecong Chen, Xiaoyan Chen, Shuyue Luo, Wenhua Jiang, Jiaojiao Jiang, Ming Yang

Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China.Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China. · pubmed

Paper

Abstract

Sarcopenic obesity (SO), characterized by the coexistence of obesity and sarcopenia, is an increasingly prevalent condition in aging populations, associated with numerous adverse health outcomes. We aimed to identify and validate an explainable prediction model of SO using easily available clinical characteristics.

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Research summary

The paper presents a machine learning model to predict sarcopenic obesity using clinical features. Sarcopenic obesity is a significant concern in aging populations, impacting health outcomes and longevity.

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